1. Field of the Invention
This invention relates to a memory system and a memory method as well as robotic apparatus, and is preferably applicable to an entertainment robot for example.
2. Description of the Rerlated Art
There has been proposed a system utilizing a competitive neural network as an associative memory system applicable to entertainment robots.
In this associative memory system, as shown in FIG. 12, input patterns composed of combinations of recognized results (hereinafter, referred to as “ID” as in color IDs, shape IDs, and face IDs, etc.) of each identifying system about several factors (for example, color, shape, and face, etc.) regarding an event or a thing (a symbol) being an object, are stored as memory patterns P1–Pn, and as shown in FIG. 13, the input of a certain pattern (a key pattern) recollects and outputs memory patterns P1–Pn analogous to it from among the same memory patterns P1–Pn stored. Note that in FIGS. 12 and 13 the squares positioned at the same height in the input patterns and the memory patterns P1–Pn signify the same factors, and that differences in pattern in the same factor indicate that results recognized from it differ.
Therefore, according to such an associative memory system as this, when a pattern similar to memory patterns P1–Pn stored in memory process is entered, a complete pattern complemented with the information of a missing factor can be output so that it may be possible to associate the identified result of the face with the name of a particular person for example.
FIG. 14 shows a typical example of the structure of an associative memory system to which a competitive neural network is applied. As shown in FIG. 14, this competitive neural network is structured as a hierarchical neural network composed of two sets of layers: input layers and competitive layers.
In this case, there are arranged in a set of input layers the number of input neurons equivalent to the aggregate of the number (m pieces) of IDs regarding the factor 1, the number (n pieces) of IDs regarding the factor 2, and so forth. And, each input neuron is assigned an identified result ID1–IDm, ID1–IDn, . . . from a relevant identifying system.
Also, a plurality of competitive neurons are provided in a set of competitive layers, each competitive neuron connected to each input neuron in the input layers with a certain degree of connection weight. Each of these competitive neurons is equivalent to one symbol (a conceptual object) to be stored. Note that the connection weight of the input layers and the competitive layers takes any value of from zero (0) to one (1) with the initial connection weight determined at random.
A competitive neural network provides two operation modes, namely a memory mode and a recollecting mode; in the memory mode input patterns are stored competitively, and in the recollecting mode complete patterns P1–Pn (FIGS. 12 and 13) is recollected from an input pattern missing partially.
Storing such a competitive neural network in the memory mode is performed by selecting a competitive neuron having fought it out in the competitive layers for an input pattern desired to be stored, and by strengthening a connection between the competitive neuron and each input neuron.
Here, in the input pattern vector [x1, x2, . . . , xn], the input neuron x1 corresponds to the first identified result (ID1) of the identifying system in regard to Factor 1, and the input of the first identified result ignites the input neuron X1, giving rise to such ignition on other factors in succession as well. An ignited input neuron takes the value of “1”, a not-ignited input neuron the value of “−1”.
And, assuming the connection weight of the ith input neuron and the jth competitive neuron as Wij, the value of a competitive neuron y1 against the input x1 is expressed by the following Expression (1):
                              y          i                =                              ∑                          i              =              0                        NumOfInput                    ⁢                                    W              ji                        ⁢                          X              i                                                          (        1        )            
Therefore, a competitive neuron winning out the competition can be sought by the following Expression (2):max {yi}  (2)
Also, in this manner the updating of the connection weight Wji of the competitive neuron winning out the competition and the input neuron is performed by the following Expression (3), in accordance with the Kohonen's updating rules:ΔWji=α(Xi−Wji)α: Learning RatioWji (new)−ΔWji+Wji (old)  (3)
At this point, regulating it with L2Norm leads to the following Expression (4):
                                          W            ji                    ⁡                      (            new            )                          =                                            W              ji                        ⁡                          (              new              )                                                                          ∑                i                NumOfInout                                      ⁢                          W              ji              2                                                          (        4        )            
Consequently the connection weight Wji (new) obtained in the Expression (4) denotes the strength of a new memory after being updated, which comes to be a retentive memory.
Note that in Expression (4), the learning ratio α is a parameter denoting the relations between the number of times presented and memory. The greater the learning ratio α is, what is once retained will not be forgotten, and so, when a like pattern is presented next time, the retained pattern can be associated with, almost without fail.
Next, the recollecting mode is described. Assume that a certain input pattern vectors [x1, x2, . . . , xn] are presented. These input pattern vectors [x1, x2. . . , xn] can be an ID, or its likelihood or probability.
At this time, the value of the output neuron yi is calculated as in the following Expression (5); relative to the input pattern vector x1:
                              y          i                =                              ∑                          i              =              0                        NumOfInput                    ⁢                                    W              ji                        ⁢                          X              i                                                          (        5        )            
This Expression (5) can also be interpreted as indicating the likelihood of the igniting value of a competitive neuron corresponding to the likelihood of each factor. What is important here is that as for the likelihood inputs of a plurality of factors, the whole likelihood can be sought by connecting those likelihood inputs.
Now, supposing that it is the sole one with the greatest associated likelihood that is selected, the output neuron yi that can win competition can be obtained by the following Expression (6):max {yi}  (6)
And, as the number of the competitive neuron Y obtained in this way corresponds to the number of the symbol memorized, the input pattern X can be recollected by performing an inverse matrix operation on W as in the following Expression (7):Y=W·XX=W−1·Y=WT·Y  (7)
Thanks to the employment of a structure making memory stronger by gradually increasing the connection weight between the input neuron and the competitive neuron as described above, an associative memory system employing such a competitive neural network is designed to be capable of performing statistical addition-learning on each symbol corresponding to each competitive neuron respectively. Therefore, a conventional associative memory system using such a conventional competitive neuron network has a merit of being strong against noise.
When viewed from the opposite standpoint, however, because of that very structure, an associative memory system using such a conventional competitive neural network could perform learning on the symbol as statistical addition-learning only, and therefore, has a disadvantage of memorizing even clear information only by slow degrees.
Also, since a neural network is used in a conventional associative memory system employing a competitive neural network, it is necessary to increase the numbers of input neurons and competitive neurons accordingly so as to increase the number of types of information that can be stored and the maximum number of symbols that is memorized, causing a disadvantage of a substantial increase in the volume of calculation and the quantity of occupancy physical memory.